Method for realizing fingerprint spectrum characterization of polygonatum cyrtonema through novel bifunctional nano platform combining metabolic spectrum and tissue imaging

Abstract

The invention relates to the technical field of metabonomics analysis, in particular to a method for realizing fingerprint characterization of polygonatum cyrtonema through a novel bifunctional nano-platform combining a metabolic spectrum and tissue imaging. According to the invention, the application of MXene-GO-Au in the high-performance LDI-MS substrate is realized for the first time. The machine learning technology can efficiently extract features from large-scale mass spectrum data, carry out dimension reduction analysis and identify key metabolite differences. In combination with a mode recognition and classification algorithm, the ML can effectively process complex high-dimensional data, and accurate distinguishing of samples is achieved. On the basis, MXene-GO-Au assisted LDI-MS analysis and a machine learning method are combined, and an efficient and reliable technical means can be provided for metabolite difference identification of polygonatum cyrtonema under different cultivation modes. The strategy not only helps to rapidly discriminate samples from different cultivation sources and realize scientific evaluation and traceability management of the quality of polygonatum cyrtonema, but also provides data support for standardized planting and resource development of medicinal plants, and has important application prospects and popularization values.

Description

Technical Field

[0001] The present invention relates to the technical field of metabolomics analysis, and particularly relates to a method for characterizing the fingerprint of Polygonatum cyrtonema Hua by using a novel bifunctional nanoplatform that combines metabolic profiling and tissue imaging. Background Art

[0002] Different cultivation methods can lead to differences in the quality and pharmacological activities of medicinal plants, posing challenges to resource development. Polygonatum cyrtonema Hua belongs to the Liliaceae family and has been recorded in "Compendium of Materia Medica" since ancient times. In recent years, Polygonatum cyrtonema Hua has been listed as a "medicinal and edible homologous" species and has high medicinal and edible values. Polygonatum cyrtonema Hua is rich in various active ingredients such as polysaccharides, steroidal saponins, and flavonoids, and has pharmacological effects such as antioxidant, anti-aging, immunomodulatory, and antibacterial and anti-inflammatory effects. Due to the high medicinal and nutritional values of wild Polygonatum cyrtonema Hua, over-excavation has led to resource shortages and price increases, prompting the emergence of various cultivation methods such as under-forest cultivation and field planting. Different cultivation methods trigger changes in metabolite profiles, thereby affecting the medicinal efficacy and quality, but relevant research is still scarce. Currently, although the commonly used liquid chromatography-mass spectrometry (LC-MS) can analyze different cultivated samples, it has problems such as complex preparation, low throughput, and large solvent consumption. Therefore, the present invention uses Polygonatum cyrtonema Hua as a model to explore a method for quickly and accurately analyzing the effects of different cultivation methods to help improve the quality of medicinal plants.

[0003] Matrix-assisted laser desorption / ionization mass spectrometry (MALDI-MS) is widely used in the analysis of medicinal plants due to its fast detection speed, small sample size, high sensitivity, and large throughput. It can not only sensitively detect compounds but also visualize the distribution of components in tissues without labeling. However, traditional organic matrices such as DHB and CHCA have low molecular background interference and uneven crystallization, affecting the detection of small molecules and repeatability. Therefore, inorganic nanomaterials have been introduced into MALDI-MS to improve metabolite detection by utilizing their characteristics such as field-enhanced electron transfer, thermal desorption, surface roughness, and simple synthesis. Although metal nanoparticles, metal oxides, carbon materials, etc. have been applied, they each have problems such as ionization selectivity, easy aggregation, or surface instability. In contrast, the emerging two-dimensional material MXene performs excellently in LDI-MS, especially in the processes of thermal desorption and charge transfer, due to its excellent electrical conductivity, high charge mobility, and good dispersibility. However, a single MXene material is prone to self-stacking, resulting in a reduction in active sites and a decrease in detection sensitivity.

[0004] To solve this problem, the present invention combines MXene with graphene oxide (GO) and gold nanoparticles (Au) to prepare a MXene-GO-Au ternary composite material. GO provides electrostatic repulsion to prevent stacking, while improving dispersibility and stability; Au NPs enhance laser energy coupling and ionization efficiency through the surface plasmon resonance (SPR) effect. The synergistic effect of the three efficiently assists laser desorption / ionization and extracts the metabolic fingerprint of Polygonatum cyrtonema Hua, thus significantly improving the detection sensitivity of small molecule metabolites. At the same time, the obtained film has excellent uniformity, enabling uniform deposition of analytes or tissue sections and improving imaging quality. For the first time, the application of MXene-GO-Au in high-performance LDI-MS substrates is realized. Machine Learning (ML) technology can efficiently extract features from large-scale mass spectrometry data, perform dimensionality reduction analysis, and identify key metabolite differences. Combining pattern recognition and classification algorithms, ML can effectively process complex high-dimensional data and achieve accurate discrimination of samples. Based on this, the combination of MXene-GO-Au-assisted LDI-MS analysis and machine learning methods can provide an efficient and reliable technical means for identifying metabolite differences in Polygonatum cyrtonema Hua under different cultivation methods. This strategy not only helps to quickly distinguish samples from different cultivation sources, realize the scientific evaluation and traceability management of the quality of Polygonatum cyrtonema Hua, but also provides data support for the standardized cultivation and resource development of medicinal plants, with important application prospects and promotion value. Summary of the Invention

[0005] The object of the present invention is: Understanding the component differences of Polygonatum sibiricum Red. under different cultivation methods is of great significance for its medicinal and edible development, but there is a lack of fast, simple, and accurate methods. To solve this problem, the present invention provides a method for realizing the fingerprint characterization of Polygonatum cyrtonema Hua by combining a novel dual-functional nanoplatform of metabolic profiling and tissue imaging. This method is based on a dual-functional detection platform of laser desorption / ionization mass spectrometry (LDI-MS) and mass spectrometry imaging (MSI) of a MXene-GO-Au composite matrix, and is used for the rapid identification and spatial distribution analysis of metabolite differences in Polygonatum cyrtonema Hua under different cultivation methods. This platform is easy to prepare, has high sensitivity, low background noise, and good repeatability. Combining machine learning (Ridge regression model) can accurately distinguish samples from different sources and screen key metabolites. This technology provides an efficient and reliable new means for the quality control and traceability management of medicinal plants.

[0006] The present invention is realized through the following technical solutions: In the first aspect, the present invention provides an application of a novel composite nanomaterial-assisted LDI MS combined with machine learning to analyze the component differences of Polygonatum cyrtonema Hua under three different cultivation methods, and further confirms the reliability of the results obtained based on the metabolomics of Polygonatum cyrtonema Hua at the primary tissue level through a composite matrix film-assisted LDI-MSI.

[0007] A method for characterizing the fingerprint of Polygonatum cyrtonema Hua by a novel dual-functional nanoplatform combining metabolic profiling and tissue imaging, comprising the following steps: Step 1, prepare MXene-GO-Au composite material and configure it into a suspension; Step 2, use MXene-GO-Au as matrix-assisted LDI MS to extract the metabolic fingerprint of Polygonatum cyrtonema Hua samples under different cultivation methods; Step 3, perform machine learning analysis on the extracted metabolic fingerprints, construct a sample discrimination model and screen key differential metabolites; Step 4, prepare MXene-GO-Au film, and combine LDI MSI to analyze the metabolite content and spatial distribution at the tissue level.

[0008] Further, step 1 specifically includes: Step 1.1, mix Ti3AlC2 MAX phase powder with LiF at a mass ratio of 1:1, add hydrochloric acid to make the liquid-solid volume ratio 20 mL / g, and etch at 35 °C for 24 hours; Step 1.2, wash and centrifuge with ultrapure water multiple times until the supernatant is nearly neutral, and filter to obtain multi-layer Ti3C2; Step 1.3, under argon protection, ultrasonically exfoliate the obtained multi-layer Ti3C2 to prepare a single-layer Ti3C2 solution, and dilute it to 1 mg / mL; Step 1.4, mix the graphene oxide (GO) solution with a mass concentration of 1 mg / mL and the Ti3C2 solution at a volume ratio of 1:30, and treat it under ultrasonic stirring for 20 minutes to prepare MXene-GO composite material; Step 1.5, slowly drop 0.1M HAuCl4 solution into the MXene-GO solution, stir, and in-situ self-reduce to form gold nanoparticles Au NPs loaded to form MXene-GO-Au composite material.

[0009] Further, in step 1.5, the dosage of HAuCl4 solution is 800 μL, the reaction condition is dark, the reaction temperature is room temperature, the reaction time is 30 minutes, the centrifugation condition is centrifugation at 8000 rpm for 5 min, and after washing three times, it is redispersed with deionized water.

[0010] Further, step 2 specifically includes: Step 2.1, collect Polygonatum cyrtonema Hua samples from different cultivation methods, cut them into small pieces respectively, freeze-dry for 72 hours, crush them, and sieve them through a 20-mesh sieve; Step 2.2: Weigh 100 mg of the sample, add it to a mixed solvent of ethanol / water (80 / 20, v / v), and under temperature-controlled conditions, perform ultrasonic treatment for 60 minutes; Step 2.3: Centrifuge to obtain the supernatant, filter it with a 0.22 mm filter membrane, and mix it with the internal standard ABA in a ratio of 1:1; Step 2.4: Take 1 μL of the MXene-GO-Au solution and spot it on an LDI MS target plate, and dry it at room temperature; Step 2.5: Take 1 μL of the mixed sample obtained in Step 2.3, drop it on the surface of the matrix, dry it, and then perform LDI-MS detection to extract the metabolic fingerprint.

[0011] Furthermore, the detection conditions for MALDI-MS in Step 2.5 are as follows: The instrument is a Rapiflex MALDITissuetyper™ TOF / TOF mass spectrometer; equipped with a 355 nm Smartbeam-II laser; the laser frequency is 5000 Hz, and the detection range is m / z 60 - 1000; a DHB / CHCA mixed matrix is used for mass spectrometry calibration.

[0012] Furthermore, Step 3 specifically includes: Step 3.1: Use FlexAnalysis Version 4.0 (Bruker Daltonik GmbH) software to perform peak extraction, baseline calibration, smoothing, peak alignment, and normalization on the metabolic fingerprint obtained in Step 2.5; Step 3.2: Use machine learning algorithms such as Ridge Regression, Gradient Boosting Machine (GBM), Least Absolute Shrinkage (Lasso), Support Vector Machine (SVM), glmBoost, XGBoost, and plsRglm to build models; Step 3.3: Evaluate the performance of the model through indicators such as precision, recall, F1 score, accuracy, and AUC, and screen the optimal model for sample analysis.

[0013] Furthermore, finally determine that Ridge Regression is the optimal model (such as Figure 7(as shown). Using the intensity of 591 normalized m / z peaks extracted from three groups of Polygonatum cyrtonema Hua samples as input features, a ridge regression model was constructed, where the feature matrix had m / z peaks as columns and samples as rows. Model training and parameter optimization were completed using the glmnet package in R language. This package uses the L2 regularization method to effectively alleviate the multicollinearity problem in high-dimensional data and reduce the risk of model overfitting. The regularization parameter λ was automatically optimized by performing ten-fold cross-validation on the training set. The dataset was randomly divided into a 60% training set and a 40% independent test set to ensure that the training data and test data did not overlap. The cross-validation process was only carried out in the training set for model fitting and hyperparameter tuning.

[0014] Furthermore, the criteria for screening differential metabolites in step 3 were: Foldchange (FC) ≥ 2 or ≤ 0.5, p-value < 0.05, and VIP value ≥ 1.

[0015] Furthermore, step 4 specifically included: Step 4.1: Drop the MXene-GO-Au solution onto the ITO glass slide to form a uniform thin film; Step 4.2: Cut the rhizome tissues of Polygonatum cyrtonema Hua at the same position into tuber pieces of similar size, freeze them in liquid nitrogen, and then section them with a thickness of 16 mm; Step 4.3: Transfer the tissue sections to the surface of the MXene-GO-Au thin film and dry and fix them; Step 4.4: Perform LDI MSI analysis under the condition of 100 mm spatial resolution.

[0016] Furthermore, the imaging target metabolites included 14 metabolites: L-aspartic acid, glycine, isoliquiritigenin, 5,4-dihydroxyflavone, dioscin, L-glutamine, L-pyroglutamic acid, ferulic acid, phloridzin, daidzein, quinine, octanoic acid, stigmasterol, and quinine.

[0017] In the second aspect, the present invention provides the application of the MXene-GO-Au composite material, and the application is selected from any one of the following 1)-4): 1) The application for metabolite analysis of medicinal plant samples with different cultivation methods; 2) The application in accurately identifying medicinal plants with different cultivation methods; 3) The application in understanding the pharmacodynamic mechanism of medicinal plants with different cultivation methods based on the screened differential markers; 4) The application for in-situ visualization analysis of medicinal plant metabolites.

[0018] Compared with the existing technology, the excellent features of the present invention are 1. The novel composite nanomaterial MXene-GO-Au-assisted LDI MS has the following advantages: 1) MXene has excellent electrical conductivity, high charge mobility, and good dispersibility, significantly improving the pyrolysis desorption efficiency and ion transfer ability; 2) The functional groups of GO generate electrostatic repulsion, preventing the stacking of MXene and enhancing the dispersibility and stability; 3) Relying on the self-reduction property of MXene, uniformly dispersed gold nanoparticles can be in-situ anchored without external reducing agents, avoiding aggregation; 4) During the preparation process, the ternary composite material MXene-GO-Au exhibits a synergistic effect. GO, as a spacer, improves the film uniformity and mechanical stability, promotes interfacial charge transfer, and gold nanoparticles enhance the laser energy coupling through the SMSI effect and SPR effect, significantly improving the detection sensitivity of small molecule metabolites. The overall design solves the stacking problem while maintaining the electrical conductivity of MXene, and improves the ionization efficiency through the synergistic effect, enabling the matrix to perform excellently in terms of sensitivity, reproducibility, quantification ability, background cleanliness, and analyte coverage, and capable of efficiently extracting the metabolic fingerprint of Polygonatum cyrtonema Hua.

[0019] 2. The present invention identifies 14 significantly different small molecule metabolites through machine learning of the fingerprint spectra of Polygonatum cyrtonema Hua under three different cultivation methods, verifies the levels of these known metabolites in the tissue parts of the three groups of Polygonatum cyrtonema Hua based on MXene-GO-Au film-assisted LDI MSI, and analyzes the imaging distribution of 14 known differential metabolites. The present invention can be widely applied to the fields of identification of cultivation methods, quality control, origin traceability, and metabolite spatial distribution analysis of Polygonatum cyrtonema Hua and other traditional Chinese medicines, and has the advantages of simple operation, high sensitivity, and strong scalability, and can provide effective support for the development and utilization of traditional Chinese medicine resources. Brief Description of the Drawings

[0020] The present invention will be further described below in conjunction with the drawings.

[0021] The drawings are schematic structural diagrams of the present invention.

[0022] Figure 1 It is a scanning transmission electron microscope photograph of the novel composite nanomaterial MXene-GO-Au in Example 1 of the present invention.

[0023] Figure 2 It is the X-ray photoelectron spectrum of the novel composite nanomaterial MXene-GO-Au in Example 1 of the present invention.

[0024] Figure 3 It is the X-ray diffraction of the novel composite nanomaterial MXene-GO-Au in Example 1 of the present invention.

[0025] Figure 4UV-Vis absorption spectra of MXene-GO-Au, MXene-GO, MXene, and GO of Example 1 of the present invention.

[0026] Figure 5 Fourier transform infrared spectrum of the novel composite nanomaterial MXene-GO-Au of Example 1 of the present invention.

[0027] Figure 6 Mass spectra of Polygonatum cyrtonema Hua under three different cultivation methods of DTZZ, LXZZ, and HJYS obtained by MXene-GO-Au-assisted LDI MS of Example 2 of the present invention.

[0028] Figure 7 Receiver operating characteristic curves of the training group and the test group of the machine learning model of Example 3 of the present invention.

[0029] Figure 8 Heat map of the first 50 features of three groups of Polygonatum cyrtonema Hua samples of Example 3 of the present invention.

[0030] Figure 9 Box plots of the contents of 14 identified metabolites in three groups of Polygonatum cyrtonema Hua samples of Example 3 of the present invention.

[0031] Figure 10 SEM on the surface of MXene-GO-Au film, and contact angle results of MXene, MXene-GO, and MXene-GO-Au of Example 4 of the present invention.

[0032] Figure 11 Optical photographs of Polygonatum cyrtonema Hua, Safranin O-Fast Green (SOFG) staining images, and spatial distribution images of 14 differential metabolites in three groups of Polygonatum cyrtonema Hua of Example 4 of the present invention. Detailed implementation manners

[0033] To make the technical content and advantages of the present invention clearer, the following detailed description is given with reference to the preferred embodiments and in conjunction with the accompanying drawings.

[0034] Example 1 Preparation method of the novel composite nanomaterial MXene-GO-Au: (1) Dissolve 2 g of LiF in 40 mL of HCl, stir evenly, add 2 g of Ti3AlC2 powder, and stir and react at 35 °C for 24 h. After the reaction, wash with ultrapure water until the solution is nearly neutral, and collect the obtained multi-layer Ti3C2 precipitate. Redisperse the precipitate in deionized water, and ultrasonically treat it for 3 h under argon protection to obtain monolayer Ti3C2 nanosheets; then centrifuge at 3500 rpm for 1 h, and collect the supernatant to obtain monolayer Ti3C2 solution.

[0035] Graphene oxide (GO) was synthesized by the improved Hummers method. Specifically: After drying graphite and KMnO4 at 80 °C for 24 h respectively, 3 g of graphite was added to a mixed solution containing 360 mL of H2SO4 and 40 mL of H3PO4 under stirring conditions. After stirring for 1 h, 18 g of KMnO4 was slowly added, and the mixture was stirred overnight at 50 °C. After the reaction, the mixture was poured into 400 mL of ice water and stirred for 30 min, and then 15 mL of 30% H2O2 was added to terminate the reaction. The obtained solid was centrifuged at 5000 rpm for 10 min, washed three times with 5% HCl solution, and then subjected to dialysis and ultrasonic treatment to obtain a stable and uniform GO suspension. Subsequently, 1 mg / mL of MXene solution (30 mL) was mixed with 1 mg / mL of GO solution (different volumes: 3.33, 5.3, 7.5, 10, 12.8 mL), and ultrasonically stirred for 15 min to obtain MXene-GO solution. Then, 800 μL of 0.1 M HAuCl4 solution was slowly added dropwise to the above mixed system, and the color of the solution changed from black to purplish red. The self-reduction reaction was continued by stirring in the dark for 30 min. After the reaction, it was centrifuged at 8000 rpm for 5 min, washed three times, and the obtained precipitate was resuspended in pure water to obtain the final MXene-GO-Au composite material solution.

[0036] (2) The microstructure and elemental composition of the composite material were studied in depth, and the results are as Figures 1-5 shown. Figure 1 is the TEM result of the composite material. The single-layer MXene nanosheets are transparent and evenly dispersed, without obvious stacking, and GO presents a wrinkled sheet structure. In the composite material, wrinkled GO and evenly dispersed Au nanoparticles (AuNPs) can be seen. High-resolution TEM shows that the lattice fringe spacing of the composite is 0.2387 nm, corresponding to the (111) crystal plane of Au, which confirms the successful growth of AuNPs on the surface of MXene-GO. EDS energy spectrum analysis shows that Ti, C, O, and Au elements are evenly distributed in the composite material. Figure 2 is the X-ray photoelectron spectroscopy result of MXene-GO-Au. XPS analysis further confirms the existence of elements. Among them, the C1s spectral peak is divided into four components: C-Ti, C-C / C=C, O-C, and O-C=O; the O1s spectral peak contains the characteristics of Ti-O, C=O, and C-O-C bonds; the Ti2p spectral peak indicates the existence of TiO2, Ti 3+ 、Ti 2 + 、Ti-C, and TiO 2-x and other various forms, among which Ti 2+The reducibility promoted the generation of AuNPs. The Au 4f spectral peaks were located at 84.19 eV and 87.82 eV respectively, attributing to the 4f 7 / 2 and 4f 5 / 2 orbitals, further verifying the formation of gold nanoparticles. The above structural characteristics contributed to improving the enrichment and ionization efficiency of the composite material for small molecule analytes, thus enhancing the detection sensitivity of LDI-MS. Figure 3 In the XRD pattern of, the (002) plane diffraction peak of MXene appeared at 2θ = 6.8°, and the (001) plane diffraction peak of GO appeared at 2θ = 10.8°. The position of the (002) peak of MXene in the composite material shifted slightly upward and the peak intensity decreased, indicating that the interlayer spacing increased after the combination of MXene and GO. No diffraction peak of rGO was observed, indicating that GO was not reduced. At the same time, the corresponding (111), (200), (220), and (311) planes of AuNPs appeared at 38.1°, 44.4°, 64.6°, and 77.7° respectively, further corroborating the successful loading of Au NPs.

[0037] (3)Characterization of the optical properties of the composite material: As Figure 4 shown, GO showed absorption peaks related to the π–π transition of the C=C bond and the n–π transition of the carboxyl group at 230 nm and 300 nm. The characteristic peak of the MXene-GO composite material showed a red shift at 230 nm, reducing the energy required for electron transition. MXene-GO-Au showed an obvious surface plasmon resonance (SPR) absorption peak at 550 nm, indicating that the AuNPs were well dispersed and there was no obvious agglomeration phenomenon. At a wavelength of 355 nm, MXene-GO-Au showed higher absorbance than MXene, GO, and the MXene-GO precursor, which was suitable for MALDI-TOF-MS analysis. Figure 5 is the FT-IR spectrum of the composite material. There were characteristic functional groups such as -OH (3431.8 cm -1 ), C=O (1631 cm -1 ), C-O-C (1096.9 cm -1 ), and Ti-O (559.8 cm -1 ) on the material surface, and there was no significant change after the combination. These functional groups not only contributed to the stable dispersion of the suspension but also promoted the effective combination with small molecule analytes, thus significantly improving the detection performance of LDI-MS.

[0038] Example 2 The composite nanomaterial MXene-GO-Au obtained in Example 1 was prepared into a 1 mg / mL solution with deionized water and used for laser desorption / ionization mass spectrometry (LDI MS) detection of Polygonatum cyrtonema Hua under three different cultivation methods. Twenty-three wild (HJYS) samples (3-4 years old) of Polygonatum cyrtonema Hua were collected from Qichun County, Huanggang City, Hubei Province, and twenty-two field cultivation (DTZZ) samples (3-4 years old); twenty-two understory growth (LXZZ) samples (3-4 years) were collected from Yangxin County, Huangshi City, Hubei Province.

[0039] (1) Cut the fresh Pseudomonas cytoplasmic samples into appropriate small pieces, freeze-dry for 72 h, and pulverize through a 20-mesh sieve. Weigh 100 mg of the obtained cytoplasmic powder into a 5 mL centrifuge tube and add ethanol / water (80 / 20, v / v). Under temperature control conditions, sonicate the solution for 60 min.

[0040] (2) Then, collect the supernatant through a 0.22 μm organic filter membrane and mix the supernatant with 25 μg of ABA in a 1:1 ratio.

[0041] (3) All MALDI MS analyses were performed on a Rapiflex MALDI Tissuetyper™ TOF / TOF mass spectrometer, in conjunction with a smartbeam-II laser configured in positive ion mode, with a wavelength of 355 nm. The laser was emitted at a frequency of 5000 hz to facilitate obtaining the original mass spectrum in the range of 60 - 1000 m / z. A mixture of DHB and CHCA substrates (Bruker Daltonics, USA) was used for mass spectrometry calibration. Transfer 1 μL of the MXene-GO-Au matrix to a stainless-steel ground sample plate. After it is completely dry, add 1 μL of the Polygonatum cyrtonema Hua extract to the film for LDI-MS analysis. Use FlexAnalysis software (Bruker Daltonics) to generate a peak map, as Figure 6 shown.

[0042] Example 3 Machine learning was performed on the metabolic fingerprint spectra of the three groups of Polygonatum cyrtonema Hua extracts to construct a discrimination model for different cultivation methods and identify significantly different metabolites: (1) The original data was imported using FlexAnalysis Version 4.0 (Bruker Daltonik GmbH), and peak extraction, baseline calibration, signal smoothing, peak alignment, and normalization were performed. Subsequently, unsupervised principal component analysis (PCA) and heatmap analysis were used to preliminarily evaluate the metabolic differences among the three groups of samples. All machine learning algorithms were completed in the R environment. During the modeling process, algorithms such as Ridge regression (Ridge), Gradient Boosting Machine (GBM), Least Absolute Shrinkage (Lasso), Support Vector Machine (SVM), glmBoost, XGBoost, and plsRglm were applied, and the performance of each model was evaluated in combination with precision, recall, F1-score, accuracy, and AUC metrics. Finally, Ridge regression (Ridge) was determined to be the optimal model (see Figure 7 ). A Ridge regression model was established using the intensity of 591 normalized m / z peaks extracted from three groups of Polygonatum cyrtonema samples as input features. The feature matrix had m / z peaks as columns and samples as rows, and model training and parameter optimization were completed through the glmnet package in R. This package uses L2 regularization to effectively suppress multicollinearity in high-dimensional data and reduce the risk of overfitting. The regularization parameter (lambda) was automatically optimized by performing ten-fold cross-validation on the training set. The dataset was randomly divided into a 60% training set and a 40% independent test set to ensure that the training and test data did not overlap. Cross-validation was performed on the training set for model fitting and hyperparameter tuning.

[0043] (2) To prove that the differences in metabolites among the three groups were statistically significant, the following cut-off values were used for filtering: FC > 2 or FC < 0.5, p < 0.05, VIP ≥ 1. To better interpret the mass spectrometry information in the analysis dataset of the three groups of Polygonatum cyrtonema, 50 characteristic metabolites with the highest projection (VIP) scores were selected from the three sample groups as key differential metabolites, and a related heatmap was generated, as shown in Figure 8 . After removing the unknown metabolites of m / z, 14 metabolites were identified and matched with public databases (Metlim, HMDB, and MassBank, Lipid Maps).

[0044] (3) Figure 9The box plots of the contents of 14 differential metabolites are shown. These metabolites include 4 amino acids (L-aspartic acid, L-pyroglutamic acid, L-glutamine, and glycine), 4 flavonoids (5,4-dihydroxyflavone, isoliquiritigenin, daidzein, and phloridzin), 2 steroid saponins (dioscin and diosgenin), and other substances. The results show that the contents of L-aspartic acid, glycine, and L-pyroglutamic acid in the HJYS group are higher than those in the DTZZ and LXZZ groups. These amino acids play a role in enhancing stress resistance and disease resistance, which is helpful for the growth and development of HJYS in the natural environment. As the main active ingredients, there are also differences in the contents of saponins in the three groups of samples: the content of dioscin in HJYS is higher than that in the other two groups, while diosgenin is more abundant in DTZZ and LXZZ. In terms of flavonoids, the contents of 5,4-dihydroxyflavone, daidzein, and isoliquiritigenin in the DTZZ group are relatively high, and these components have potential medicinal values such as antioxidant, anti-inflammatory, and blood pressure-lowering effects. Ferulic acid and phloridzin are relatively abundant in DTZZ and HJYS, but their contents are relatively low in LXZZ, which may be related to different cultivation environments and geographical factors. Caprylic acid, quinine, and L-glutamine are distributed in all three groups, but the overall content in the DTZZ group is the highest. Through comprehensive analysis, the key differential metabolites in the DTZZ and HJYS groups are L-aspartic acid, L-pyroglutamic acid, 5,4-dihydroxyflavone, and isoliquiritigenin. These changes may be related to artificial intervention, the use of pesticides or fertilizers. These differential metabolites can not only be used as potential biomarkers to distinguish Polygonatum cyrtonema Hua cultivated by different methods, but also provide a reference basis for further understanding the influence of cultivation methods on the quality of Polygonatum cyrtonema Hua.

[0045] Example 4 For the 14 differential metabolites obtained in Example 3, verification was carried out at the tissue level based on MXene-GO-Au-assisted LDI-MSI: (1) The MXene-GO-Au film was prepared by an in-situ self-assembly technique. Figure 10 Figure 9 shows the SEM and contact angle results of the MXene-GO-Au film. The surface uniformity of the substrate was measured by SEM, and it was found that the substrate had good uniformity, which was beneficial to improving the uniformity of analyte deposition. The hydrophilicity and hydrophobicity of the MXene-GO-Au substrate surface were evaluated by measuring the water contact angle. It was found that the water contact angle of the composite substrate was higher than that of the MXene and MXene-GO films. This modification can effectively improve the surface hydrophobicity of the film, limit the delocalization of analytes on the film surface, reduce the detection deviation of LDI-MSI, and is beneficial to improving the repeatability of analytes. (2)For LDI-MSI analysis, 0.6 mL of the MXene-GO-Au material (5 mg / mL) was dropped onto an ITO glass slide to form a MXene-GO-Au thin film. Three groups of Polygonatum cyrtonema Hua rhizomes at the same location were cut into tuber pieces of appropriate size and frozen in liquid nitrogen for 20 s. The frozen rhizome tissues were cut into 16-μm-thick slices at -18 °C using a cryostat (Thermo CryoStar NX50 NOVPD, Bremen, Germany) and directly transferred onto the surface of the ITO conductive glass with the MXene-GO-Au thin film. The rhizome slices were dried for 10 minutes for LDI MSI analysis.

[0046] (3)Although MXene-GO-Au-assisted LDI MS combined with machine learning can reflect the metabolic differences among the extracts of the three groups of Polygonatum cyrtonema Hua, due to the possible dilution of metabolite concentrations during the extraction process, MXene-GO-Au was further used as a substrate to assist LDI MSI in verifying the distribution levels of known differential metabolites in tissue sites. Through MXene-GO-Au-assisted LDI MSI with a spatial resolution of 100 μm, imaging analysis was performed on Polygonatum cyrtonema Hua samples obtained by three different cultivation methods, achieving a balance between imaging clarity and detection sensitivity. As Figure 11 shown, MXene-GO-Au-assisted LDI MSI successfully detected the imaging distributions of 14 known differential metabolites in the tissues of the three groups of Polygonatum cyrtonema Hua. The results showed that L-aspartic acid, glycine, and L-pyroglutamic acid were more significantly expressed in the HJYS group, while metabolites such as L-glutamine, ferulic acid, phloridzin, quinine, and isoliquiritigenin were more abundant in the DTZZ group. The spatial abundance distributions of metabolites in these tissue sections were highly consistent with the results of metabolic fingerprint analysis obtained by combining LDI-MS with machine learning. The above results not only verified the mutual verification based on tissue imaging and metabolic analysis but also further emphasized the feasibility of using MXene-GO-Au-assisted LDI MS combined with machine learning for metabolic profiling. Overall, MXene-GO-Au-assisted LDI MSI demonstrated high sensitivity and excellent resolution, proving its potential as a powerful spatial metabolomics platform.

[0047] (4)With the identification of key differential metabolites, the research further focused on their spatial distributions in the rhizome tissues of the three groups of Polygonatum cyrtonema Hua. As Figure 11As shown, L-glutamine, ferulic acid, phloridzin, daidzein, quinine, octanoic acid, and isoliquiritigenin are distributed in all parts of the rhizomes of Polygonatum cyrtonema Hua in the three groups. In contrast, glycine, L-aspartic acid, L-pyroglutamic acid, and stigmasterol are mainly concentrated in the ground tissue and vascular bundles, with less distribution in the epidermis and cortex, which may be related to the roles of stigmasterol and some amino acids in enhancing the resistance of root tissues and promoting the transport and storage of nutrients. Interestingly, quinine is evenly distributed in the HJYS group, while in the DTZZ and LXZZ groups, it is mainly concentrated in the ground tissue and vascular bundles; 5,4-dihydroxyflavone is mainly distributed in the ground tissue and vascular bundles in the DTZZ group, while in the LXZZ and HJYS groups, it is mainly present in the epidermis and cortex. The relatively high level of 5,4-dihydroxyflavone in the epidermis and cortex may help the plant resist pathogen invasion and alleviate biotic and abiotic stresses. Previous studies have shown that different cultivation methods can affect the content and spatial distribution pattern of metabolites in plants. Using the MXene-GO-Au film as a matrix, high-sensitivity SALDI MSI imaging of Polygonatum cyrtonema Hua tissues was achieved. MXene-GO-Au-assisted LDI MSI has high sensitivity, low background, good film flatness, and a wide metabolite coverage range, providing a powerful tool for observing the tissue-specific distribution of endogenous metabolites in complex biological samples.

[0048] In this paper, a novel high-sensitivity MXene-GO-Au composite material was successfully prepared. When used as a matrix to analyze small molecule metabolites, it showed low background interference, high sensitivity, strong response, good stability, and good salt and protein tolerance, enabling the quantitative detection of small molecule metabolites in samples. By applying the MXene-GO-Au film-assisted LDI MS technology, the fingerprint spectra of extracts of Polygonatum cyrtonema Hua under different cultivation methods (22 samples in the HJYS group, 23 samples in the DTZZ group, and 23 samples in the LXZZ group) were successfully obtained. Combining machine learning analysis, the differential metabolites in the three groups of samples were screened, and relative quantitative analysis was performed on 14 known key metabolites (amino acids, flavonoids, total glycosides, alkaloids, etc.). In addition, due to the uniform deposition of the matrix, MXene-GO-Au-assisted LDI MSI significantly improved the signal sensitivity and spatial resolution of metabolite imaging. The results of SALDI MSI and LDI MS on the differences in tissue space metabolite abundances were consistent, further verifying the feasibility of the MXene-GO-Au-assisted LDI MS and MSI dual-functional platform in differential metabolite analysis. At the same time, this platform can also be used for the spatial distribution analysis of metabolites in Polygonatum cyrtonema Hua, providing a basis for a deeper understanding of its metabolic characteristics. Overall, the MXene-GO-Au-assisted LDI dual-functional platform provides new ideas for quickly and simply identifying metabolic differences in medicinal plants under different cultivation methods and has important application potential.

[0049] The above embodiments of the present invention are only preferred embodiments, intended to illustrate the present invention rather than limit its protection scope. Any improvements, substitutions or deformations made by those of ordinary skill in the art through logical deduction and experimental verification without departing from the spirit and essence of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for characterizing the fingerprint of Polygonatum cyrtonema Hua by using a novel dual-functional nanoplatform that combines metabolic profiling and tissue imaging, characterized in that, It includes the following steps: Step 1: Prepare MXene-GO-Au composite material and configure it into a suspension; Step 2: Use MXene-GO-Au as a matrix-assisted LDI MS to extract the metabolic fingerprint spectra of Polygonatum cyrtonema samples under different cultivation methods; Step 3: Conduct machine learning analysis on the extracted metabolic fingerprint spectra, construct a sample discrimination model and screen key differential metabolites; Step 4: Prepare MXene-GO-Au film, and combine LDI MSI to analyze the metabolite content and spatial distribution at the tissue level.

2. The method according to claim 1, characterized in that, Step 1 specifically includes: Step 1.1: Mix Ti3AlC2 MAX phase powder with LiF at a mass ratio of 1:1, add hydrochloric acid to make the liquid-solid volume ratio 20 mL / g, and etch at 35 °C for 24 hours; Step 1.2: Wash and centrifuge with ultrapure water multiple times until the supernatant is nearly neutral, and filter to obtain multi-layer Ti3C2; Step 1.3: Under argon protection, ultrasonically exfoliate the obtained multi-layer Ti3C2 to prepare a single-layer Ti3C2 solution, and dilute it to 1 mg / mL; Step 1.4: Mix the graphene oxide (GO) solution with a mass concentration of 1 mg / mL and the Ti3C2 solution at a volume ratio of 1:30, and treat it under ultrasonic stirring conditions for 20 minutes to prepare MXene-GO composite material; Step 1.5: Slowly drop 0.1 M HAuCl4 solution into the MXene-GO solution, stir, and in-situ self-reduce to generate gold nanoparticles Au NPs loading to form MXene-GO-Au composite material.

3. The method according to claim 2, characterized in that, In Step 1.5, the dosage of HAuCl4 solution is 800 μL, the reaction condition is darkness, the reaction temperature is room temperature, the reaction time is 30 minutes, the centrifugation condition is centrifugation at 8000 rpm for 5 min, and after washing three times, it is redispersed with deionized water.

4. The method according to claim 1, wherein Step 2 specifically includes Step 2.1: Collect Polygonatum cyrtonema samples from different cultivation methods, cut them into small pieces respectively, freeze-dry for 72 hours, crush them, and screen them through a 20-mesh sieve; Step 2.2: Weigh 100 mg of the sample, add ethanol / water (80 / 20, v / v) mixed solvent, and ultrasonically treat it for 60 minutes under temperature control conditions; Step 2.3: Centrifuge to take the supernatant, filter it with a 0.22 mm filter membrane, and mix it with the internal standard ABA at a ratio of 1:1; Step 2.4: Take 1 mL of MXene-GO-Au solution and spot it on the LDI MS target plate, and dry it at room temperature; Step 2.5: Take 1 μL of the mixed sample obtained in Step 2.3 and drop it on the surface of the matrix, dry it and then conduct LDI-MS detection to extract the metabolic fingerprint spectra.

5. The method according to claim 4, wherein The detection conditions of MALDI-MS in Step 2.5 are: the instrument is Rapiflex MALDI Tissuetyper™ TOF / TOF mass spectrometer; equipped with a 355 nm Smartbeam-II laser; the laser frequency is 5000 Hz, the detection range is m / z 60 - 1000; a DHB / CHCA mixed matrix is used for mass spectrometry calibration.

6. The method according to claim 1, wherein Step 3 specifically includes: Step 3.1: Perform peak extraction, baseline calibration, smoothing, peak alignment, and normalization on the metabolic fingerprint obtained in Step 2.5 using FlexAnalysis Version 4.0 (Bruker Daltonik GmbH) software; Step 3.2: Build models using machine learning algorithms such as Ridge Regression, Gradient Boosting Machine (GBM), Least Absolute Shrinkage and Selection Operator (Lasso), Support Vector Machine (SVM), glmBoost, XGBoost, and plsRglm; Step 3.3: Evaluate the model performance through metrics such as precision, recall, F1-score, accuracy, and AUC, and select the optimal model for sample analysis.

7. The method according to claim 6, characterized in that, The optimal model selected in Step 3.3 is the Ridge Regression model. The model takes 591 normalized m / z peak intensities as input features. The feature matrix has m / z peaks as columns and samples as rows, and is established using the glmnet software package in R language and adopts the L2 regularization method. Among them, the regularization parameter λ is automatically optimized through ten-fold cross-validation on the training set. The dataset is divided into a 60% training set and a 40% test set, and the cross-validation is only performed on the training set.

8. The method according to claim 6, characterized in that, The criteria for screening differential metabolites in Step 3 are: Foldchange (FC) ≥ 2 or ≤ 0.5, p-value < 0.05, and VIP value ≥ 1.

9. The method according to claim 1, wherein Step 4 specifically includes: Step 4.1: Drop the MXene-GO-Au solution onto the ITO glass slide to form a uniform thin film; Step 4.2: Cut the rhizome tissue of Polygonatum cyrtonema Hua at the same position into tuber pieces of similar size, freeze them in liquid nitrogen, and then slice them with a thickness of 16 mm; Step 4.3: Transfer the tissue slices to the surface of the MXene-GO-Au thin film and dry and fix them; Step 4.4: Perform LDI MSI analysis under the condition of a 100 mm spatial resolution; Among them, the imaging target metabolites include 14 metabolites: L-aspartic acid, glycine, isoliquiritigenin, 5,4-dihydroxyflavone, dioscin, L-glutamine, L-pyroglutamic acid, ferulic acid, phloridzin, daidzein, quinine, octanoic acid, stigmasterol, and quinine.

10. An application of the MXene-GO-Au composite material according to any one of claims 1 to 9, characterized in that, The application is selected from any one of the following 1)-4): 1) Application for metabolite analysis in medicinal plant samples with different cultivation methods; 2) Application in accurately identifying medicinal plants with different cultivation methods; 3) Application in understanding the pharmacodynamic mechanisms of medicinal plants with different cultivation methods based on the screened differential markers; 4) Application for in-situ visualization analysis of medicinal plant metabolites.

Images